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This article proposes a dynamic evolving fuzzy system (DEFS) for streaming data prediction. DEFS utilizes the enhanced data potential and prediction errors of individual local models as the main criteria for fuzzy rule generation. A vital feature of the proposed system is its novel rule merging scheme that can self-adjust its tolerance toward the degree of similarity between two similar fuzzy rules according to the size of the rule base. To better handle the shifts and drifts in the data patterns, a novel rule quality measure based on both the utility values and the prediction accuracy of individual fuzzy rules is further introduced to help DEFS identify these less activated fuzzy rules with poorer descriptive capabilities and, thereby, maintaining a healthier fuzzy rule base by removing these stale rules. Very importantly, the thresholds used by DEFS are self-adaptive toward the input data. The adaptive thresholds can help DEFS to precisely capture the underlying structure and dynamically changing patterns of streaming data, enabling the system to perform accurate approximation reasoning. Numerical examples based on several popular benchmark problems show the superior performance of DEFS over the state-of-the-art evolving fuzzy systems. The prediction performance of the proposed method is at least 2.88% better than the best-performing comparative EFSs on each individual regression benchmark problem considered in this study, and the average performance improvement across all the numerical experiments is approximately 30%.

Fuzzy systems offer a formal and practically popular methodology for modeling nonlinear problems with inherent uncertainties, entailing strong performance and model interpretability. Particularly, semisupervised boosting is widely recognized as a powerful approach for creating stronger ensemble classification models in the absence of sufficient labeled data without introducing any modification to the employed base classifiers. However, the potential of fuzzy systems in semisupervised boosting has not been systematically explored yet. In this study, a novel semisupervised boosting algorithm devised for zero-order evolving fuzzy systems is proposed. It ensures both the consistence among predictions made by individual base classifiers at successive boosting iterations and the respective levels of confidence toward their predictions throughout the process of sample weight updating and ensemble output generation. In so doing, the base classifiers are empowered to gradually focus more on challenging samples that are otherwise hard to generalize, enabling the development of more precise integrated classification boundaries. Numerical evaluations on a range of benchmark problems are carried out, demonstrating the efficacy of the proposed semisupervised boosting algorithm for constructing ensemble fuzzy classifiers with high accuracy.

Tropical cyclones (TCs), with an intensive wind pump impact, induce sea surface temperature cooling (SSTC) on the upper ocean. SSTC is a pronounced indicator to reveal TC evolution and oceanic conditions. However, there are few effective methods for accurately approximating the amplitude of the spatial structure of TC-induced SSTC. This study proposes a novel explainable machine learning framework to model and interpret the amplitude of the spatial structure of SSTC over the northwest Pacific (NWP). In particular, 12 predictors related to TC characteristics and pre-storm ocean states are considered as inputs. A composite analysis technique is used to characterize the amplitude of the spatial structure of SSTC across the TC track. Extreme gradient boosting (XGBoost) is utilized to predict the amplitude of SSTC from the 12 predictors. To better interpret the ocean–atmosphere interaction, a SHapely Additive explanations (SHAP) method is further employed to identify the contributions of predictors in determining the amplitude of the TC-induced SSTC, bringing the attribute-oriented explainability to the proposed method. The results showed that the proposed method could accurately predict the amplitude of the spatial structure of SSTC for different TC intensity groups and outperforms a numerical model. The proposed method also serves as an effective tool for reconstructing composite maps of both interannual and seasonal evolutions of SSTC spatial structure. The study offers insight into applying machine learning to model and interpret the responses of oceanic conditions triggered by extreme weather conditions (e.g., TCs).

It is widely recognized that learning systems have to go deeper to exchange for more powerful representational learning capabilities in order to precisely approximate nonlinear complex problems. However, the best-known computational intelligence approaches with such characteristics, namely, deep neural networks, are often criticized for lacking transparency. In this article, a novel multilayer evolving fuzzy neural network (MEFNN) with a transparent system structure is proposed. The proposed MEFNN is a metalevel stacking ensemble learning system composed of multiple cascading evolving neuro-fuzzy inference systems (ENFISs), processing input data layer-by-layer to automatically learn multilevel nonlinear distributed representations from data. Each ENFIS is an evolving fuzzy system capable of learning from new data sample by sample to self-organize a set of human-interpretable IF– THEN fuzzy rules that facilitate approximate reasoning. Adopting ENFIS as its ensemble component, the multilayer system structure of the MEFNN is flexible and transparent, and its internal reasoning and decision-making mechanism can be explained and interpreted to/by humans. To facilitate information exchange between different layers and attain stronger representation learning capability, the MEFNN utilizes error backpropagation to self-update the consequent parameters of the IF–THEN rules of each ensemble component based on the approximation error propagated backward. To enhance the capability of the MEFNN to handle complex problems, a nonlinear activation function is introduced to modeling the consequent parts of the IF–THEN rules of ENFISs, thereby empowering both the representation and the reflection of nonlinearity in the resulting fuzzy outputs. Numerical examples on a wide variety of challenging (benchmark and real-world) classification and regression problems demonstrate the superior practical performance of the MEFNN, revealing the effectiveness and validity of the proposed approach.

Ensemble learning is a widely used methodology to build powerful predictors from multiple individual weaker ones. However, the vast majority of ensemble learning models are designed for offline application scenarios, the use of evolving fuzzy systems in ensemble learning for online learning from data streams has not been sufficiently explored, yet. In this paper, a novel self-adaptive fuzzy learning ensemble system is introduced for data stream prediction. The proposed ensemble system employs the very sparse random projection technique to compress the consequent parts of the learned fuzzy rules by individual base models to a more compressed form, thereby reducing redundant information and improving computational efficiency. To improve the overall prediction performance, a dynamical base model pruning scheme is introduced to the proposed ensemble system together with a novel inferencing scheme, such that less accurate base models will be removed from the ensemble structure at each learning cycle automatically and only these more accurate ones will be involved in joint decision-making. Numerical examples based on a wide range of benchmark datasets demonstrate the stronger prediction performance of the proposed ensemble system over the state-of-the-art alternatives. [ABSTRACT FROM AUTHOR]

Evolving intelligent systems (EISs), particularly, the zero-order ones have demonstrated strong performance on many real-world problems concerning data stream classification while offering high model transparency and interpretability thanks to their prototype-based nature. Zero-order EISs typically learn prototypes by clustering streaming data online in a “one pass” manner for greater computation efficiency. However, such identified prototypes often lack optimality, resulting in less precise classification boundaries, thereby hindering the potential classification performance of the systems. To address this issue, a commonly adopted strategy is to minimize the training error of the models on historical training data or alternatively to iteratively minimize the intracluster variance of the clusters obtained via online data partitioning. This recognizes the fact that the ultimate classification performance of zero-order EISs is driven by the positions of prototypes in the data space. Yet, simply minimizing the training error may potentially lead to overfitting while minimizing the intracluster variance does not necessarily ensure the optimized prototype-based models to attain improved classification outcomes. To achieve better classification performance while avoiding overfitting for zero-order EISs, this article presents a novel multiobjective optimization approach, enabling EISs to obtain optimal prototypes via involving these two disparate but complementary strategies simultaneously. Five decision-making schemes are introduced for selecting a suitable solution to deploy from the final nondominated set of the resulting optimized models. Systematic experimental studies are carried out to demonstrate the effectiveness of the proposed optimization approach in improving the classification performance of zero-order EISs.

Nowadays, cyber-attacks have become a common and persistent issue affecting various human activities in modern societies. Due to the continuously evolving landscape of cyber-attacks and the growing concerns around "black box" models, there has been a strong demand for novel explainable and interpretable intrusion detection systems with online learning abilities. In this paper, a novel soft prototype-based autonomous fuzzy inference system (SPAFIS) is proposed for network intrusion detection. SPAFIS learns from network traffic data streams online on a chunk-by-chunk basis and autonomously identifies a set of meaningful, human-interpretable soft prototypes to build an IF-THEN fuzzy rule base for classification. Thanks to the utilization of soft prototypes, SPAFIS can precisely capture the underlying data structure and local patterns, and perform internal reasoning and decision-making in a human-interpretable manner based on the ensemble properties and mutual distances of data. To maintain a healthy and compact knowledge base, a pruning scheme is further introduced to SPAFIS, allowing itself to periodically examine the learned solution and remove redundant soft prototypes from its knowledge base. Numerical examples on public network intrusion detection datasets demonstrated the efficacy of the proposed SPAFIS in both offline and online application scenarios, outperforming the state-of-the-art alternatives. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach to the self-organization of hierarchical prototype-based classifiers from data is proposed. The approach recursively partitions the data at multiple levels of granularity into shape-free clusters of different sizes, resembling Voronoi tessellation, and naturally aggregates the resulting cluster medoids into a multi-layered prototype-based structure according to their descriptive abilities. Different from conventional classification models, it is nonparametric and entirely data-driven, and the learned model can offer a high-level of transparency and interpretability thanks to the underlying prototype-based nature. The system identification process underpinning the approach is driven by the aim of separating data samples of different classes into nonoverlapping multi-granular clusters. Its associated decision-making process follows the "nearest prototype" principle and hence, the rationales of the subsequent decisions made can be explicitly explained. Experimental studies based on popular benchmark classification problems, as well as on a practical application to remote sensing image classification, demonstrate the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

• A novel semi-supervised self-organizing fuzzy inference system (S3OFIS+) is introduced for data stream classification. • A novel parameter-free pseudo-labelling strategy is proposed for S3OFIS+ to effectively self-train from unlabelled data. • The proposed S3OFIS+ can self-learn from unlabelled data streams chunk-by-chunk in a single-pass manner with minimum human input. • The proposed S3OFIS+ can provide users high interpretability and explainability thanks to its prototype-based nature. As a powerful tool for data streams processing, the vast majority of existing evolving intelligent systems (EISs) learn prediction models from data in a supervised manner. However, high-quality labelled data can be difficult to obtain in many real-world classification applications concerning data streams, though unlabelled data is plentiful. To overcome the labelling bottleneck and construct a stronger classification model, a novel semi-supervised EIS is proposed in this paper. After being primed with a small amount of labelled data, the proposed method is capable of continuously self-developing its system structure and self-updating the meta-parameters from unlabelled data streams chunk-by-chunk in a non-iterative, exploratory manner by exploiting a novel pseudo-labelling strategy. Thanks to its transparent prototype-based structure and human-understandable reasoning process, the proposed method can provide users high explainability and interpretability while achieving great classification precision. Experimental investigation demonstrates the superior performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a novel self-adaptive fuzzy learning (SAFL) system is proposed for streaming data prediction. SAFL self-learns from data streams a predictive model composed of a set of prototype-based fuzzy rules, with each of which representing a certain local data distribution, and continuously self-evolves to follow the changing data patterns in non-stationary environments. Unlike conventional evolving fuzzy systems, both the fuzzy inference and consequent parameter learning schemes utilised by SAFL are simplified so that only a small number of selected fuzzy rules within the rule base are involved in system output generation and parameter updating during a learning cycle. Such simplification not only significantly reduces the system's computational complexity but also increases its prediction precision. In addition, both theoretical and empirical investigations guarantee the stability of the resulting SAFL. Comparative experimental studies on a wide variety of benchmark and real-world problems demonstrate that SAFL is able to learn from streaming data in a highly efficient manner and to make predictions with a great accuracy, revealing the effectiveness and validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Testis differentiation is initiated when Sryin pre-Sertoli cells directs the gonad toward a male-specific fate. Sertoli cells are essential for testis development, but cell types within the interstitial compartment, such as immune and endothelial cells, are also critical for organ formation. Our previous work implicated macrophages in fetal testis morphogenesis, but little is known about genes underlying immune cell development during organogenesis. Here, we examine the role of the immune-associated genes Mafband Mafin mouse fetal gonad development, and we demonstrate that deletion of these genes leads to aberrant hematopoiesis manifested by supernumerary gonadal monocytes. Mafb; Mafdouble knockout embryos underwent initial gonadal sex determination normally, but exhibited testicular hypervascularization, testis cord formation defects, Leydig cell deficit, and a reduced number of germ cells. In general, Mafband Mafalone were dispensable for gonad development; however, when both genes were deleted, we observed significant defects in testicular morphogenesis, indicating that Mafband Mafwork redundantly during testis differentiation. These results demonstrate previously unappreciated roles for Mafband Mafin immune and vascular development and highlight the importance of interstitial cells in gonadal differentiation.Deletion of Mafband Mafgenes leads to supernumerary monocytes in fetal mouse gonads, resulting in vascular, morphogenetic, and differentiation defects during testicular organogenesis.

Prototype-based approaches generally provide better explainability and are widely used for classification. However, the majority of them suffer from system obesity and lack transparency on complex problems. In this paper, a novel classification approach with a multi-layered system structure self-organized from data is proposed. This approach is able to identify local peaks of multi-modal density derived from static data and filter out more representative ones at multiple levels of granularity acting as prototypes. These prototypes are then optimized to their locally optimal positions in the data space and arranged in layers with meaningful dense links in-between to form pyramidal hierarchies based on the respective levels of granularity accordingly. After being primed offline, the constructed classification model is capable of self-developing continuously from streaming data to self-expend its knowledge base. The proposed approach offers higher transparency and is convenient for visualization thanks to the hierarchical nested architecture. Its system identification process is objective, data-driven and free from prior assumptions on data generation model with user- and problem- specific parameters. Its decision-making process follows the "nearest prototype" principle, and is highly explainable and traceable. Numerical examples on a wide range of benchmark problems demonstrate its high performance. [ABSTRACT FROM AUTHOR]

This paper introduces a novel self-training hierarchical prototype-based approach for semi-supervised classification. The proposed approach firstly identifies meaningful prototypes from labelled samples at multiple levels of granularity and, then, self-organizes a highly transparent, multi-layered recognition model by arranging them in a form of pyramidal hierarchies. After this, the learning model continues to self-evolve its structure and self-expand its knowledge base to incorporate new patterns recognized from unlabelled samples by exploiting the pseudo-label technique. Thanks to its prototype-based nature, the overall computational process of the proposed approach is highly explainable and traceable. Experimental studies with various benchmark image recognition problems demonstrate the state-of-the-art performance of the proposed approach, showing its strong capability to mine key information from unlabelled data for classification. [ABSTRACT FROM AUTHOR]

In this paper, a novel hierarchical prototype-based approach for classification is proposed. This approach is able to perceive the data space and derive the multimodal distributions from streaming data at different levels of granularity in an online manner, based on which it further identifies meaningful prototypes to self-organize and self-evolve its hierarchical structure for classification. Thanks to the prototype-based nature, the system structure of the proposed classifier is highly transparent, and its learning process is of "one pass" type and computationally lean. Its decision-making process follows the "nearest prototype" principle and is fully explainable. The proposed approach is capable of presenting the learned knowledge from data in an easy-to-interpret prototype-based hierarchical form to users, and is an attractive tool for solving large-scale, complex real-world problems. Numerical examples based on various benchmark problems justify the validity and effectiveness of the proposed concept and general principles. [ABSTRACT FROM AUTHOR]

Optimality of the premise, IF part is critical to a zero-order evolving intelligent system (EIS) because this part determines the validity of the learning results and overall system performance. Nonetheless, a systematic analysis of optimality has not been done yet in the state-of-the-art works. In this paper, we use the recently introduced self-organising neuro-fuzzy inference system (SONFIS) as an example of typical zero-order EISs and analyse the local optimality of its solutions. The optimality problem is firstly formulated in a mathematical form, and detailed optimality analysis is conducted. The conclusion is that SONFIS does not generate a locally optimal solution in its original form. Then, an optimisation method is proposed for SONFIS, which helps the system to attain local optimality in a few iterations using historical data. Numerical examples presented in this paper demonstrate the validity of the optimality analysis and the effectiveness of the proposed optimisation method. In addition, it is further verified numerically that the proposed concept and general principles can be applied to other types of zero-order EISs with similar operating mechanisms. [ABSTRACT FROM AUTHOR]

In allusion to heterogeneous multi-criteria group decision making (MCGDM) problems with incomplete weights and q -rung orthopair fuzzy (q -ROF) truth degrees, where many kinds of criteria values, i.e., crisp values, intervals, trapezoidal fuzzy values, hesitant fuzzy values and q -ROF values (q -ROFVs), and multiple types of interactions exist, i.e., positive synergetic interactions, negative synergetic interactions and independence, a novel multi-objective q -ROF programming approach is proposed. In particular, in order to globally capture the interactions among criteria, Choquet-based relative closeness degrees are developed based on the technique for order performance by similarity to ideal solution (TOPSIS) and the Choquet integral. Then, the q -ROF Choquet-based group consistency index (q -ROFCGCI) and the q -ROF Choquet-based group inconsistency index (q -ROFCGII) are defined. Next, to derive optimal 2-additive fuzzy measures on the criteria set and optimal experts' weights, a new multi-objective q -ROF mathematical programming model is established by minimizing the q -ROFCGII and maximizing the q -ROFCGCI. Subsequently, an algorithm based on the adaptive non-dominated sorting genetic algorithm III (A-NSGA-III) is designed to solve the established model. Afterwards, the Choquet-based overall relative closeness degrees of the alternatives are used to obtain their preferred ordering. Finally, the effectiveness and advantage of the proposed approach is verified using four real cases concerning the evaluation of social commerce. • Develop the Choquet-based relative closeness degree. • Construct the q -rung orthopair fuzzy group consistency and inconsistency indices. • Propose a novel multi-objective q -rung orthopair fuzzy programming approach. • Design an algorithm to solve the multi-objective fuzzy programming model. • Apply the proposed approach to solve the evaluation of social commerce. [ABSTRACT FROM AUTHOR]

The thermal design of highly reliable modular multilevel converters (MMCs) is significant for the voltage-source converter based high-voltage direct current (VSC-HVdc) systems, especially under the ac unbalanced fault. In this paper, the surplus submodule is employed as new control freedom to optimize the thermal behavior of the MMCs, because its number is linearly increased with the ac voltage dip. With the surplus submodule active bypassed (SSAB), the total submodules of the MMC in one arm can be taken turns to reduce the thermal stress of the hottest power device. In order to obtain equal ac voltages in three phases to improve the SSAB strategy, the neutral-point-shift (NPS) scheme is introduced under the unbalanced conditions, especially single-phase-to-ground fault. Through the balancing of the amplitude of the MMC ac voltages with the NPS scheme, the thermal stress asymmetry of theMMC under the single-phaseto-ground fault is mitigated, and the junction temperature of the most stressed power devices is significantly reduced. Finally, the simulation and experiment results show that the junction temperature rise of the most stressed power device is reduced by nearly 30% with the SSAB plus NPS scheme.

In this paper, a new compact, vialess and vertical microstrip-to-microstrip differential transition is presented based on half-wavelength slotline structure. The equivalent-circuit model is constructed, and theoretical analyses based on equivalent circuit model reveal that good differential mode (DM) signal transmission between the top- and bottom-layer differential microstrip lines can be achieved over a wide frequency range determined while the common mode (CM) signal is rejected. Moreover, the systematic design method is presented to extract the values of electrical parameters from prescribed performance in terms of its operating bandwidth and ripple levels. In addition, owing to the employment of half-wavelength slot, the proposed differential transition has a compact size. Finally, the proposed differential transition and design theories are well vindicated by an exemplary circuit centered at 2.45 GHz with the measured relative frequency bandwidth (FBW) of 114 %.

In this paper, a new type of multilayer rule-based classifier is proposed and applied to image classification problems. The proposed approach is entirely data-driven and fully automatic. It is generic and can be applied to various classification and prediction problems, but in this paper we focus on image processing, in particular. The core of the classifier is a fully interpretable, understandable, self-organized set of IF…THEN… fuzzy rules based on the prototypes autonomously identified by using a one-pass type training process. The classifier can self-evolve and be updated continuously without a full retraining. Due to the prototype-based nature, it is non-parametric; its training process is non-iterative, highly parallelizable and computationally efficient. At the same time, the proposed approach is able to achieve very high classification accuracy on various benchmark datasets surpassing most of the published methods, be comparable with the human abilities. In addition, it can start classification from the first image of each class in the same way as humans do, which makes the proposed classifier suitable for real-time applications. Numerical examples of benchmark image processing demonstrate the merits of the proposed approach. [ABSTRACT FROM AUTHOR]

This study proposes to construct a model using random forest method, an efficient machine learning‐based method, to predict the spatial structure and temporal evolution of the sea surface temperature (SST) cooling induced by northwest Pacific tropical cyclones (TCs), a process of the so‐called wind pump. The predictors in use include 12 predictors related to TC characteristics and pre‐storm ocean conditions. The model is shown to skillfully predict the spatiotemporal evolutions of the cold wake generated by TCs of different intensity groups, and capture the cross‐case variance in the observed SST response. Another model is further built based on the same method to assess the relative importance of the 12 predictors in determining the magnitude of the maximum cooling. Computations of feature scores of those predictors show that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate, depending on the area where the cooling is considered. While many studies have been devoted to understanding the processes and mechanisms underlying the sea surface temperature (SST) cooling induced by tropical cyclones (TCs), few studies have attempted to predict the spatial and temporal evolution of the sea surface temperature (SST) cooling triggered by TCs. In this study, we proposed to achieve this goal by building a model using an efficient and robust machine learning‐based method. The constructed model uses 12 predictors associated with TC characteristics (e.g., intensity, and translation speed) and pre‐storm ocean states (e.g., mixed layer depth). The model performs well in producing the TC‐induced spatial structure and temporal evolution of the cold wake and can capture most of the variance in the observed SST response. We quantified the relative importance of the 12 predictors, and found that TC intensity, translation speed and size, and pre‐storm mixed layer depth and SST dominate in deciding the magnitude of the SST response. The results and proposed method have important implications for predicting the response of ocean primary production to the TC wind pump effects. A machine learning‐based model is built to predict the spatiotemporal evolution of the tropical cyclone‐induced sea surface temperature responseThe model well predicts the spatial structure and temporal evolution of the observed response and captures the observed cross‐case varianceFeature scores are computed to assess the relative importance of the predictors in determining the magnitude of the SST response A machine learning‐based model is built to predict the spatiotemporal evolution of the tropical cyclone‐induced sea surface temperature response The model well predicts the spatial structure and temporal evolution of the observed response and captures the observed cross‐case variance Feature scores are computed to assess the relative importance of the predictors in determining the magnitude of the SST response

In this paper, we propose a fully autonomous, local-modes-based data partitioning algorithm, which is able to automatically recognize local maxima of the data density from empirical observations and use them as focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. The method is free from user- and problem- specific parameters and prior assumptions. The proposed algorithm has two versions: i) offline for static data and ii) evolving for streaming data. Numerical results based on benchmark datasets prove the validity of the proposed algorithm and demonstrate its excellent performance and high computational efficiency compared with the state-of-art clustering algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present a self-organising nonparametric fuzzy rule-based classifier. The proposed approach identifies prototypes from the observed data through an offline training process and uses them to build a 0-order AnYa type fuzzy rule-based system for classification. Once primed offline, it is able to continuously learn from the streaming data afterwards to follow the changing data pattern by updating the system structure and meta-parameters recursively. The meta-parameters of the proposed approach are derived from data directly. By changing the level of granularity, the proposed approach can make a trade-off between performance and computational efficiency, and, thus, the classifier is able to address a wide variety of problems with specific needs. The classifier also supports different types of distance measures. Numerical examples based on benchmark datasets demonstrate the high performance of the proposed approach and its ability of handling high-dimensional, complex, large-scale problems. [ABSTRACT FROM AUTHOR]

In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware ( SODA ) data partitioning and forming data clouds is proposed. The proposed SODA algorithm employs an extra cosine similarity-based directional component to work together with a traditional distance metric, thus, takes the advantages of both the spatial and angular divergences. Using the nonparametric Empirical Data Analytics (EDA) operators, the proposed algorithm automatically identifies the main modes of the data pattern from the empirically observed data samples and uses them as focal points to form data clouds. A streaming data processing extension of the SODA algorithm is also proposed. This extension of the SODA algorithm is able to self-adjust the data clouds structure and parameters to follow the possibly changing data patterns and processes. Numerical examples provided as a proof of the concept illustrate the proposed algorithm as an autonomous algorithm and demonstrate its high clustering performance and computational efficiency. [ABSTRACT FROM AUTHOR]

Coal fly ash (CFA), an industrial solid waste, has been predominantly used a substitute for material in the concrete industry. Its reactivity is usually a crucial factor for use in cementitious composites. Conventionally physical and chemical treatments have been used to modify CFA for improving reactivity, but there still are some limitations such as easy agglomeration of particles, high cost, high energy-consumption, environmental pollution, etc. Herein, a feasible bio-modification treatment on CFA using Paenibacillus mucilaginosusis proposed. Adhesion and desilication of the bacteria & metabolites change the morphology, size distribution and surface properties of CFA. Compared with CFA, the bio-modified CFA (BCFA) has coarser surface, finer particle size, larger specific surface area (SSA), higher surface calcium content and lower surface Si/Al ratio. The characteristic changes contribute to enhancing the reactivity of BCFA relating to morphological, pozzolanic, micro-aggregate and interfacial effects in cementitious composites. The strength activity index (SAI) of BCFA reaches 90.8% at 28 d and increases 27.7% compared to that of CFA. Soluble silicon content in the bioleaching system shows significantly positive correlations with the SSA and SAI of BCFA (P < 0.05). The cementitious composites containing 30% BCFA show higher mechanical properties, lower early hydration heat and denser microstructure compared to those containing 30% CFA. This work could provide a promising green and low-carbon way to produce highly active BCFA for concrete industry and other fields.

Granites are inevitably subjected to the effects of repeated water quenching in the process of geothermal energy exploitation. To gain a greater understanding of the mechanical responses and cracking mechanisms of granites affected by quenching cycling, conventional triaxial compression tests were conducted involving granite samples after high temperature treatment (150–750 °C) with multiple water quenching cycles (1–15). The findings indicated that, when the heating temperature values or quenching cycles increase, both the longitudinal wave velocity and bulk density declined, while both the mass loss and volume increase rates were enhanced. The peak intensity of diffraction for quartz exhibited a reduction of 61.38% and 27.25%, respectively. Gradually enhanced thermal damage against the increase in temperature values or quenching cycles resulted in a loose matrix structure within the granite samples with complicated crack networks. With increasing number of cycles, the peak strength exhibited a negative linear decrease, and the reduction in peak strength with increasing temperature could be divided into two stages, with a threshold temperature value of 450 °C, while the peak axial strain experienced an exponential increasing trend. Both the elastic and secant moduli decreased with the temperature and number of cycles due to primary thermal damage accumulation in the granite, and the post peak rock brittleness weakened. As the temperature levels and quenching cycles were increased, the cohesion respectively decreased by 44.12–56.99% and 27.07–36.55%, due to the weakened bonding force between mineral grains, while the Poisson's ratio increased. Defining the crack damage stress (maximum volumetric strain) as the initial yield position, variations in the plastic shear strain and subsequent strength characteristics were analysed. Both the subsequent cohesion and internal friction angle increased with the plastic shear strain but decreased with both the temperature and number of cycles. At a low temperature level, typical oblique or V-shaped shear cracks, traversing the whole sample height, resulted in ultimate sample failure, while at a larger temperature level or a larger number of cycles, the sample was characterized by progressive failure of the loose matrix structure generated by 3D crack networks, rather than large penetrating macro cracks.

Incineration bottom ash (IBA) is the residue of municipal solid waste (MSW) after incineration, which is often used for land backfilling (in the anaerobic environment). This study reports a method of anaerobic bacteria for IBA treatment, and the mechanism of heavy metals immobilization by anaerobic bacteria is discussed in depth. In this study, the IBA from three cities, namely Shenyang, Wuhan, and Guangzhou in China, were treated with anaerobic bacterial grouting, and the leaching concentrations of heavy metals were detected. Their IBA production ranks among the top ten cities in China. The results show that the anaerobic bacteria can reduce the leaching concentration of Zn, Cu, Cd, Cr, As, and Pb, and the immobilization ratios reach 50-90%. Biotreatment improves the carbonate-bound form of the noted heavy metals, and the carbonate-bound form is positively correlated with bioprecipitation of calcium carbonate. It is observed by scanning electron microscope (SEM) and optical microscope that fragmentized calcium carbonate crystals are still generated when the upper surface of the sample exposes to air. The mechanism of immobilization during the biomineralization process is revealed by the fluorescence spectrum and chemical reaction. In an anaerobic environment, the anaerobic bacteria enrich calcium ions and carbonate ions to generate biogel, which can physically envelop heavy metals. The induction of anaerobic bacteria can stabilize heavy metals by changing chemical speciation. They all reach the strength limitation of land backfill. This study provides a new idea to reduce the environmental harm from the IBA and improves the sustainable utilization of IBA.

We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility values of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the established model. Accordingly, the ranking order of alternatives is derived using the Banzhaf-based overall perceived utility values. Experimental studies pertaining to investment selection problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems. [ABSTRACT FROM AUTHOR]